Brian Granger wrote:
> Hi,
>
> i am running numpy on aix compiling with xlc. Revision 1.0rc2 works
> fine and passes all tests. But 1.0rc3 and more recent give the
> following on import:
>
Most likely the error-detection code is not working on your platform.
The platform dependent stuff i
Thanks, I will investigate more on these things and get back to you
early in the week. But for now numpy seems to be functioning pretty
normally (log(2) gives the correct answer). thanks again.
It would be great to figure this stuff out before 1.0, but we might
not have time.
Brian
On 10/20/06
Brian Granger wrote:
> Tim,
>
> I just tried everything with r3375. I set seterr(all='warn') and the
> tests passed. But all the floating point warning are still there.
> With seterr(all='ignore') the warnings go away and all the tests pass.
> should I worry about the warnings?
>
Maybe. I jus
Tim,
I just tried everything with r3375. I set seterr(all='warn') and the
tests passed. But all the floating point warning are still there.
With seterr(all='ignore') the warnings go away and all the tests pass.
should I worry about the warnings?
thanks
Brian
On 10/20/06, Tim Hochberg <[EMA
Brian Granger wrote:
> When I set seterr(all='warn') I see the following:
>
> In [1]: import numpy
> /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/ufunclike.py:46:
> RuntimeWarning: invalid value encountered in log
> _log2 = umath.log(2)
> /usr/common/homes/g/granger/usr/local/lib/py
I have been doing these recent tests with 1.0rc3. I am building from
trunk right now and we will see how that goes. Thanks for your help.
Brian
On 10/20/06, Tim Hochberg <[EMAIL PROTECTED]> wrote:
> Brian Granger wrote:
> > Also, when I use seterr(all='ignore') the the tests fail:
> >
> > =
Brian Granger wrote:
> Also, when I use seterr(all='ignore') the the tests fail:
>
> ==
> FAIL: Ticket #112
> --
> Traceback (most recent call last):
> File
> "
When I set seterr(all='warn') I see the following:
In [1]: import numpy
/usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/ufunclike.py:46:
RuntimeWarning: invalid value encountered in log
_log2 = umath.log(2)
/usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/scimath.py:19:
Runt
Also, when I use seterr(all='ignore') the the tests fail:
==
FAIL: Ticket #112
--
Traceback (most recent call last):
File
"/usr/common/homes/g/granger/usr/loca
Here is the traceback that I got:
In [1]: import numpy
---
exceptions.FloatingPointErrorTraceback (most
recent call last)
/u2/granger/
/usr/common/homes/g/granger/usr/local/lib/python/numpy/__init__.p
Brian Granger wrote:
> Hi,
>
> i am running numpy on aix compiling with xlc. Revision 1.0rc2 works
> fine and passes all tests. But 1.0rc3 and more recent give the
> following on import:
>
> Warning: invalid value encountered in multiply
> Warning: invalid value encountered in multiply
> Warning:
Hi,
i am running numpy on aix compiling with xlc. Revision 1.0rc2 works
fine and passes all tests. But 1.0rc3 and more recent give the
following on import:
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multipl
On 20/10/06, Sebastian Żurek <[EMAIL PROTECTED]> wrote:
> Is there something like that in any numerical python modules (numpy,
> pylab) I could use?
In scipy there are some very convenient spline fitting tools which
will allow you to fit a nice smooth spline through the simulation data
points (o
Sebastien Bardeau wrote:
>>One possible solution (there can be more) is using ndarray:
>>
>>In [47]: a=numpy.array([1,2,3], dtype="i4")
>>In [48]: n=1# the position that you want to share
>>In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype="i4")
>>
>>
>>
>Ok thanks. Actually that
Sebastian Żurek wrote:
> Hi!
>
> This is probably a silly question but I'm getting confused with a
> certain problem: a comparison between experimental data points (2D
> points set) and a model (2D points set - no analytical form).
>
> The physical model produces (by a sophisticated simulations
Hi!
This is probably a silly question but I'm getting confused with a
certain problem: a comparison between experimental data points (2D
points set) and a model (2D points set - no analytical form).
The physical model produces (by a sophisticated simulations done by an
external program) some 2
On 10/20/06, JJ <[EMAIL PROTECTED]> wrote:
> My suggestion is to
> create a new attribute, such as .AR, so that the
> following could be used: M[K.AR,:]
It would be even better if M[K,:] worked. Would such a patch be
accepted? (Not that I know how to make it.)
Hello.
I have a suggestion that might make slicing using
matrices more user-friendly. I often have a matrix of
row or column numbers that I wish to use as a slice.
If K was a matrix of row numbers (nx1) and M was a nxm
matrix, then I would use ans = M[K.A.ravel(),:] to
obtain the matrix I want.
Thanks for the comments, Here is the code for the new histogram, tests included. I'll wait for comments or suggestions before submitting a patch (numpy / scipy) ?CheersDavid
2006/10/18, Tim Hochberg <[EMAIL PROTECTED]>:
My $0.02:If histogram is going to get a makeover, particularly one that makes i
Sebastien Bardeau wrote:
> Ooops sorry there was two mistakes with the 'hasslice' flag. This seems
> now to work for me.
>
>
[SNIP code]
That looks overly complicated. I believe that this (minimally tested in
a slightly different setting) or some variation should work:
return self[...,newaxi
Ooops sorry there was two mistakes with the 'hasslice' flag. This seems
now to work for me.
def __getitem__(self,index): # Index may be either an int or a tuple
# Index length:
if type(index) == int: # A single element through first dimension
ilen = 1
index = (ind
Francesc Altet wrote:
> A Divendres 20 Octubre 2006 11:42, Sebastien Bardeau va escriure:
> [snip]
>
>> I can understand that numpy.scalars do not provide inplace operations
>> (like Python standard scalars, they are immutable), so I'd like to use
>>
>> 0-d Numpy.ndarrays. But:
>> >>> d = numpy
> One possible solution (there can be more) is using ndarray:
>
> In [47]: a=numpy.array([1,2,3], dtype="i4")
> In [48]: n=1# the position that you want to share
> In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype="i4")
>
Ok thanks. Actually that was also the solution I found. But t
On Fri, Oct 20, 2006 at 11:42:26AM +0200, Sebastien Bardeau wrote:
> >>> a = numpy.array((1,2,3))
> >>> b = a[:2]
Here you index by a slice.
> >>> c = a[2]
Whereas here you index by a scalar.
So you want to do
b = a[[2]]
b += 1
or in the general case
b = a[slice(2,3)]
b += 1
Regards
Stéf
Hi,
There is an operation I do a lot, I would call it "unrolling" a array.
The best way to describe it is probably to give the code:
def unroll(M):
""" Flattens the array M and returns a 2D array with the first columns
being the indices of M, and the last column the flatten M.
""
Am 20.10.2006 um 02:53 schrieb Jay Parlar:
>> Hi!
>> I try to compile numpy rc3 on Panther and get following errors.
>> (I start build with "python2.3 setup.py build" to be sure to use the
>> python shipped with OS X. I din't manage to compile Python2.5 either
>> yet with similar errors)
>> Does
A Divendres 20 Octubre 2006 11:42, Sebastien Bardeau va escriure:
[snip]
> I can understand that numpy.scalars do not provide inplace operations
> (like Python standard scalars, they are immutable), so I'd like to use
>
> 0-d Numpy.ndarrays. But:
> >>> d = numpy.array(a[2],copy=False)
> >>> d +=
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Hi!
I am confused with Numpy behavior with its scalar or 0-d arrays objects:
>>> numpy.__version__
'1.0rc2'
>>> a = numpy.array((1,2,3))
>>> b = a[:2]
>>> b += 1
>>> b
array([2, 3])
>>> a
array([2, 3, 3])
>>> type(b)
To this point all is ok for me: subarrays share (by default) memory wit
On Thu, Oct 19, 2006 at 09:03:57PM -0400, Pierre GM wrote:
> Indeed. That's basically why you have to edit your __array_finalize__ .
>
> class InfoArray(N.ndarray):
> def __new__(info_arr_cls,arr,info={}):
> info_arr_cls._info = info
> return N.array(arr).view(info_arr_cls)
>
A. M. Archibald wrote:
>On 18/10/06, Travis Oliphant <[EMAIL PROTECTED]> wrote:
>
>
>
>>If there are any cases satisfying these rules where a copy does not have
>>to occur then let me know.
>>
>>
>
>For example, zeros((4,4))[:,1].reshape((2,2)) need not be copied.
>
>I filed a bug in trac an
On 18/10/06, Travis Oliphant <[EMAIL PROTECTED]> wrote:
> If there are any cases satisfying these rules where a copy does not have
> to occur then let me know.
For example, zeros((4,4))[:,1].reshape((2,2)) need not be copied.
I filed a bug in trac and supplied a patch to multiarray.c that avoids
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